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HyperSNN: A new efficient and robust deep learning model for resource constrained control applications
In light of the increasing adoption of edge computing in areas such as
intelligent furniture, robotics, and smart homes, this paper introduces
HyperSNN, an innovative method for control tasks that uses spiking neural
networks (SNNs) in combination with hyperdimensional computing. HyperSNN
substitutes expensive 32-bit floating point multiplications with 8-bit integer
additions, resulting in reduced energy consumption while enhancing robustness
and potentially improving accuracy. Our model was tested on AI Gym benchmarks,
including Cartpole, Acrobot, MountainCar, and Lunar Lander. HyperSNN achieves
control accuracies that are on par with conventional machine learning methods
but with only 1.36% to 9.96% of the energy expenditure. Furthermore, our
experiments showed increased robustness when using HyperSNN. We believe that
HyperSNN is especially suitable for interactive, mobile, and wearable devices,
promoting energy-efficient and robust system design. Furthermore, it paves the
way for the practical implementation of complex algorithms like model
predictive control (MPC) in real-world industrial scenarios